|Topic/Title||Tutorial on "Darknets and Cryptocurrency"|
|Date & Time||t.b.a. (duration 2 h)|
|Description||Hidden anonymous networks (commonly known as "darknets") only grow more popular as the criminal underworld continues to recognize the value of these services. In a similar vein, cryptocurrency is the preferred way for criminals and terrorists to exchange funds. In this talk, we will explore these two technologies and how each are currently used by different threat groups. With the "state of the dark side" established, we will explore the new and emerging threats these technologies pose both to individuals, corporations, and governments. Techniques such as asymmetric communication channels, C2-less malware, and misattributable network intrusions will be discussed. Finally, we will discuss why our traditional defenses will fail to meet these threats, and analyze some potential solutions to counter the latest generation of threats.|
|Short Bio||Andrew Johnston is a consultant at Mandiant, a division of FireEye. Currently, he works as a proactive consultant performing red team, social engineering, and physical assessments for multinational clients. Andrew also leads a research team at Fordham University dedicated to using machine learning to eradicate Islamic extremism and narcotics trafficking online. Previously, Andrew had worked with City of Hope Hospitals, LaQuinta Hotels, Staples, and the FBI employing his data-driven approach to improving cybersecurity.|
|Topic/Title||Tutorial on "Machine Learning and Urban Analytics"|
|Date & Time||t.b.a. (duration 2 h)|
|Description||Machine Learning has promised, and delivered, a great deal of insight, efficiencies, and new opportunities to various industries and academic fields. Given the importance of government at all levels in our society, it makes sense to use machine learning to improve the operations of government, particularly in our large urban areas that have complex problems at larger scales. Applying these advanced techniques to even seemingly simple problems, however, has proven challenging for a host of reasons we will explore in this talk. Using examples from local, state, and federal government agencies attempting to integrate machine learning and other advanced techniques, we will talk about the opportunities, the challenges, and the key lessons learned from past attempts in order to make our urban areas more efficient, livable, equitable, and resilient now and into the future.|
|Short Bio||Richard Dunks is the founder of Datapolitan, an urban informatics consultancy based in New York City that focuses on the data and information needs of the public sector, including government agencies and non-profits. A graduate in the first cohort of the urban data science program at New York University's Center for Urban Science and Progress, the first program in the world devoted to data science for urban areas, Richard has been on the forefront of applying data science techniques to urban challenges through his work directly with local government agencies. Through past work with the Center for Government Excellence at Johns Hopkins University, he helped develop innovative methods of data analysis and visualization to medium-sized cities across the country. He currently teaches a series of informative and engaging classes in data analysis, information visualization, and statistics for the City of New York using open data, and has taught courses in data mining, spatial analysis, information visualization, and informatics at Columbia University, Fordham University, and Pratt Institute.|
Department of Computer Science and Informatics, Jönköping University, Sweden, email@example.com
|Topic/Title||Tutorial on "Predicting with confidence"|
|Date & Time||t.b.a. (duration approx. 2 h)|
|Description||How good is your prediction? In risk-sensitive applications, it is crucial to be able to assess the quality of a prediction, but traditional classification and regression models don't provide their users with any information regarding prediction trustworthiness. |
Conformal predictors, on the other hand, are predictive models that associate each of their predictions with a precise measure of confidence. Given a user-defined significance level E, a conformal predictor outputs, for each test pattern, a multivalued prediction region (class label set or real-valued interval) that, under relatively weak assumptions, contains the test pattern’s true output value with probability 1-E. In other words, given a significance level E, a conformal predictor makes an erroneous prediction with probability E. The conformal prediction framework allows any traditional classification or regression model to be transformed into a confidence predictor with little extra work, both in terms of implementation and computational complexity.
Some key properties of conformal prediction are:
• We obtain probabilities/error bounds per instance
• Probabilities are well-calibrated: 95% means 95%
• We don't need to know the priors
• We make a single assumption - that the data is exchangeable ~ i.i.d.
• We can apply it to any machine learning algorithm
• It is rigorously proven and straightforward to implement
• There is no magic involved – only mathematics and algorithms
Hence, confidence predictors is an important tool that every data scientist should carry in their toolboxes, and conformal prediction represents a straight-forward way of associating the predictions of any predictive machine learning algorithm with confidence measures.
This tutorial aims to provide an introduction and an example-oriented exposition of the conformal prediction framework, directed at machine learning researchers and professionals. A publicly available Python library, developed by one of the authors of the tutorial, will be used for the running examples. The goal of the tutorial is to provide attendees with the knowledge necessary for implementing functional conformal predictors, and to highlight current research on the subject.
Authors: Henrik Boström (Stockholm University), Lars Carlsson (AstraZeneca), Alex Gammerman (Royal Holloway, University of London), Ulf Johansson (Jönköping University) and Henrik Linusson (University of Borås)
|Short Bio||Prof. Ulf Johansson holds a M.Sc. in Computer Engineering and Computer Science from Chalmers University of Technology, and a PhD degree in Computer Science from the Institute of Technology, Linköping University, Sweden. |
Ulf Johansson’s research focuses on developing machine learning algorithms for data analytics. Most of the research is applied, and often co-produced with industry. Application areas include drug discovery, health science, marketing, high-frequency trading, game AI, sales forecasting and gambling. In 2011, he had his 15 minutes of fame when called as an expert witness in the Swedish Supreme Court regarding whether Poker is a game of skill or chance. In the court, Prof. Johansson argued that skill predominates over chance using, among other sources, his paper “Fish or Shark – Data Mining Online Poker”, originally presented at DMIN 2009.
Ulf Johansson has published extensively in the fields of artificial intelligence, machine learning, soft computing and data mining. He is also a regular program committee member of the leading conferences in computational intelligence and machine learning. During the last few years, Prof. Johansson has published several papers on conformal prediction, some presented in top-tier venues like the Machine Learning journal and the ICDM conference.
Invited Talk 1
|Speaker||Dr. Peter Geczy|
National Institute of Advanced Industrial Science and Technology (AIST), Japan
|Topic/Title||Data Revolution: From Data Science to Data Economy|
|Date & Time||t.b.a.|
|Description||We are witnessing data revolution. Explosion of digital data has been affecting all segments of contemporary society—from science to economy. Commercial organizations and governments have been accumulating vast volumes of diverse data. Data has become a key asset for modern technology companies and organizations. It has a significant inherent value. When properly utilized, it drives commercial revenue streams, innovation, and discovery. Realization of value of data is a notable challenge. Data Science has emerged as an interdisciplinary endeavor to tackle such challenges. Approaches and methods of data science have been extensively employed by data-oriented businesses. They have been playing a vital role in an expanding spectrum of economic activities—giving birth to new data economy. We shall explore pertinent drivers and trends at the intersection of data science and data economy.|
|Short Bio||Dr. Peter Geczy holds a senior position at the National Institute of Advanced Industrial Science and Technology (AIST). His recent research interests are in information technology intelligence. This multidisciplinary research encompasses development and exploration of future and cutting-edge information technologies. It also examines their impacts on societies, organizations and individuals. Such interdisciplinary scientific interests have led him across domains of technology management and innovation, data science, service science, knowledge management, business intelligence, computational intelligence, and social intelligence. Dr. Geczy received several awards in recognition of his accomplishments. He has been serving on various professional boards and committees, and has been a distinguished speaker in academia and industry. He is a senior member of IEEE and has been an active member of INFORMS and INNS.|
Invited Talk 2
|Speaker||Dr. Dawud Gordon|
CEO & Co-Founder at TwoSense, NYC
|Topic/Title||Applications of behavior-based authentication in the business world|
|Date & Time||t.b.a.|
|Description||This talk will look at the applications of behavior-based authentication in the business world and how it has been affected by research. We’ll look at the field as a whole, and also at the vision for us at TwoSense, and demonstrate that this is a problem that is insoluble without the application of machine learning. We will then take a deeper look at the machine learning challenges that must be overcome, and a few novel solutions from our labs. We will then look at some of the lessons learned from deploying behavioral biometrics in the wild in product settings. From there, we will look at some research methodology issues that we’ve come across and conclude by proposing a few best practices for the behavioral biometrics community.|
|Short Bio||Dr. Dawud Gordon is CEO & Co-Founder at TwoSense, a NYC-based cybersecurity startup working with Behavioral Biometrics. TwoSense uses Machine Learning to create a mobile AI that learns to recognize the user based on their behavior. This enables authentication that is actionless, so there’s nothing you have to do, continuous, so it’s always on even if you’re not interacting with the device, and more secure than a fingerprint. TwoSense changes the fundamental paradigm of identity security away from making you responsible for proving you’re the authorized user, to making the machine do the work for you. Dawud holds a Ph.D. in Computer Engineering from KIT in Karlsruhe, Germany for his work on using Machine Learning to recognize social group behaviors from sensor signals off of members’ mobile and wearable devices. He has published over 30 peer reviewed papers and patents on related topics, won several awards for his research including Best Paper, and currently serves on the Programming Committee of the International Symposium for Wearable Computing (ISWC).|